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<a href="_kernel_helpers_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Collection of functions dealing with typical tasks of kernels</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"></span> </div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"></span> </div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * </span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \author      O. Krause</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * \date        2007-2012</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> *</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * </span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * </span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * </span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> *</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment"> */</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#ifndef SHARK_MODELS_KERNELS_KERNELHELPERS_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#define SHARK_MODELS_KERNELS_KERNELHELPERS_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_kernel_function_8h.html" title="abstract super class of all kernel functions">shark/Models/Kernels/AbstractKernelFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_dataset_8h.html">shark/Data/Dataset.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;<a class="code" href="_open_m_p_8h.html">shark/Core/OpenMP.h</a>&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>    <span class="comment"></span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">///  \brief Calculates the regularized kernel gram matrix of the points stored inside a dataset.</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">///</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">///  Regularization is applied by adding the regularizer on the diagonal</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">///  \param kernel the kernel for which to calculate the kernel gram matrix</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">///  \param dataset the set of points used in the gram matrix</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">///  \param matrix the target kernel matrix</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">///  \param regularizer the regularizer of the matrix which is always &gt;= 0. default is 0.</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">///  \ingroup kernels</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType, <span class="keyword">class</span> M, <span class="keyword">class</span> Device&gt;</div>
<div class="foldopen" id="foldopen00053" data-start="{" data-end="}">
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"><a class="line" href="group__kernels.html#ga3eaca71bfc1467b79c9341dcfcad25c1">   53</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="group__kernels.html#ga3eaca71bfc1467b79c9341dcfcad25c1" title="Calculates the regularized kernel gram matrix of the points stored inside a dataset.">calculateRegularizedKernelMatrix</a>(</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a><span class="keyword">const</span>&amp; kernel,</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset,</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    blas::matrix_expression&lt;M, Device&gt;&amp; matrix,</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>    <span class="keywordtype">double</span> regularizer = 0</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>){</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(regularizer &gt;= 0, <span class="stringliteral">&quot;regularizer must be &gt;=0&quot;</span>);</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    std::size_t B = dataset.<a class="code hl_function" href="group__shark__globals.html#gabd82edf467b9b82f4b0a1e70fd695311" title="Returns the number of batches of the set.">numberOfBatches</a>();</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    <span class="comment">//get start of all batches in the matrix</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    <span class="comment">//also include  the past the end position at the end</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    std::vector&lt;std::size_t&gt; batchStart(B+1,0);</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    <span class="keywordflow">for</span>(std::size_t i = 1; i != B+1; ++i){</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>        batchStart[i] = batchStart[i-1]+ <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(i-1));</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    }</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(batchStart[B] == dataset.<a class="code hl_function" href="group__shark__globals.html#ga814e8b0028cc90dd2af69805e8f8a04d" title="Returns the total number of elements.">numberOfElements</a>());</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    std::size_t N  = batchStart[B];<span class="comment">//number of elements</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>    ensure_size(matrix,N,N);</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    </div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    </div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>    <span class="keywordflow">for</span> (std::size_t i=0; i&lt;B; i++){</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        std::size_t startX = batchStart[i];</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        std::size_t endX = batchStart[i+1];</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        <a class="code hl_define" href="_open_m_p_8h.html#a8a63d79e2c3625260e6092d933f21a98" title="Set of macros to help usage of OpenMP with Shark.">SHARK_PARALLEL_FOR</a>(<span class="keywordtype">int</span> j=0; j &lt; (int)B; j++){</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>            std::size_t startY = batchStart[j];</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>            std::size_t endY = batchStart[j+1];</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>            RealMatrix submatrix = kernel(dataset.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(i), dataset.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(j));</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>            noalias(subrange(matrix(),startX,endX,startY,endY))=submatrix;</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>            <span class="comment">//~ if(i != j)</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>                <span class="comment">//~ noalias(subrange(matrix(),startY,endY,startX,endX))=trans(submatrix);</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        }</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        <span class="keywordflow">for</span>(std::size_t k = startX; k != endX; ++k){</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>            matrix()(k,k) += <span class="keyword">static_cast&lt;</span>typename M::value_type<span class="keyword">&gt;</span>(regularizer);</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        }</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>    }</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>}</div>
</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment"></span> </div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment">///  \brief Calculates the kernel gram matrix between two data sets.</span></div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="comment">///</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="comment">///  \param kernel the kernel for which to calculate the kernel gram matrix</span></div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="comment">///  \param dataset1 the set of points corresponding to rows of the Gram matrix</span></div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="comment">///  \param dataset2 the set of points corresponding to columns of the Gram matrix</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="comment">///  \param matrix the target kernel matrix</span></div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment">///  \ingroup kernels</span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType, <span class="keyword">class</span> M, <span class="keyword">class</span> Device&gt;</div>
<div class="foldopen" id="foldopen00097" data-start="{" data-end="}">
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"><a class="line" href="group__kernels.html#ga3fafbf415f6fec4d166ade39dccbc01a">   97</a></span><span class="keywordtype">void</span> <a class="code hl_function" href="group__kernels.html#ga3fafbf415f6fec4d166ade39dccbc01a" title="Calculates the kernel gram matrix between two data sets.">calculateMixedKernelMatrix</a>(</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a><span class="keyword">const</span>&amp; kernel,</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset1,</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset2,</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    blas::matrix_expression&lt;M, Device&gt;&amp; matrix</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>){</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    std::size_t B1 = dataset1.<a class="code hl_function" href="group__shark__globals.html#gabd82edf467b9b82f4b0a1e70fd695311" title="Returns the number of batches of the set.">numberOfBatches</a>();</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>    std::size_t B2 = dataset2.<a class="code hl_function" href="group__shark__globals.html#gabd82edf467b9b82f4b0a1e70fd695311" title="Returns the number of batches of the set.">numberOfBatches</a>();</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>    <span class="comment">//get start of all batches in the matrix</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>    <span class="comment">//also include  the past the end position at the end</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>    std::vector&lt;std::size_t&gt; batchStart1(B1+1,0);</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    <span class="keywordflow">for</span>(std::size_t i = 1; i != B1+1; ++i){</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        batchStart1[i] = batchStart1[i-1]+ <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(dataset1.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(i-1));</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>    }</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>    std::vector&lt;std::size_t&gt; batchStart2(B2+1,0);</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>    <span class="keywordflow">for</span>(std::size_t i = 1; i != B2+1; ++i){</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        batchStart2[i] = batchStart2[i-1]+ <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(dataset2.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(i-1));</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>    }</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>    <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(batchStart1[B1] == dataset1.<a class="code hl_function" href="group__shark__globals.html#ga814e8b0028cc90dd2af69805e8f8a04d" title="Returns the total number of elements.">numberOfElements</a>());</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(batchStart2[B2] == dataset2.<a class="code hl_function" href="group__shark__globals.html#ga814e8b0028cc90dd2af69805e8f8a04d" title="Returns the total number of elements.">numberOfElements</a>());</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    std::size_t N1 = batchStart1[B1];<span class="comment">//number of elements</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    std::size_t N2 = batchStart2[B2];<span class="comment">//number of elements</span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>    ensure_size(matrix,N1,N2);</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>    </div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>    <span class="keywordflow">for</span> (std::size_t i=0; i&lt;B1; i++){</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        std::size_t startX = batchStart1[i];</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>        std::size_t endX = batchStart1[i+1];</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        <a class="code hl_define" href="_open_m_p_8h.html#a8a63d79e2c3625260e6092d933f21a98" title="Set of macros to help usage of OpenMP with Shark.">SHARK_PARALLEL_FOR</a>(<span class="keywordtype">int</span> j=0; j &lt; (int)B2; j++){</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>            std::size_t startY = batchStart2[j];</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>            std::size_t endY = batchStart2[j+1];</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>            RealMatrix submatrix = kernel(dataset1.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(i), dataset2.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(j));</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>            noalias(subrange(matrix(),startX,endX,startY,endY))=submatrix;</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>            <span class="comment">//~ if(i != j)</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>                <span class="comment">//~ noalias(subrange(matrix(),startY,endY,startX,endX))=trans(submatrix);</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        }</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    }</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>}</div>
</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span><span class="comment"></span> </div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment">///  \brief Calculates the regularized kernel gram matrix of the points stored inside a dataset.</span></div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span><span class="comment">///</span></div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span><span class="comment">///  Regularization is applied by adding the regularizer on the diagonal</span></div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span><span class="comment">///  \param kernel the kernel for which to calculate the kernel gram matrix</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span><span class="comment">///  \param dataset the set of points used in the gram matrix</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span><span class="comment">///  \param regularizer the regularizer of the matrix which is always &gt;= 0. default is 0.</span></div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span><span class="comment">/// \return the kernel gram matrix</span></div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span><span class="comment">///  \ingroup kernels</span></div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType&gt;</div>
<div class="foldopen" id="foldopen00144" data-start="{" data-end="}">
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"><a class="line" href="group__kernels.html#gabfe57330fd12701e94f030ff1e042ae7">  144</a></span>RealMatrix <a class="code hl_function" href="group__kernels.html#ga3eaca71bfc1467b79c9341dcfcad25c1" title="Calculates the regularized kernel gram matrix of the points stored inside a dataset.">calculateRegularizedKernelMatrix</a>(</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a><span class="keyword">const</span>&amp; kernel,</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset, </div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>    <span class="keywordtype">double</span> regularizer = 0</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>){</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(regularizer &gt;= 0, <span class="stringliteral">&quot;regularizer must be &gt;=0&quot;</span>);</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    RealMatrix M;</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>    <a class="code hl_function" href="group__kernels.html#ga3eaca71bfc1467b79c9341dcfcad25c1" title="Calculates the regularized kernel gram matrix of the points stored inside a dataset.">calculateRegularizedKernelMatrix</a>(kernel,dataset,M,regularizer);</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>    <span class="keywordflow">return</span> M;</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>}</div>
</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span><span class="comment"></span> </div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span><span class="comment">///  \brief Calculates the kernel gram matrix between two data sets.</span></div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="comment">///</span></div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span><span class="comment">///  \param kernel the kernel for which to calculate the kernel gram matrix</span></div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span><span class="comment">///  \param dataset1 the set of points corresponding to rows of the Gram matrix</span></div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span><span class="comment">///  \param dataset2 the set of points corresponding to columns of the Gram matrix</span></div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span><span class="comment">///  \return matrix the target kernel matrix</span></div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span><span class="comment">///  \ingroup kernels</span></div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType&gt;</div>
<div class="foldopen" id="foldopen00163" data-start="{" data-end="}">
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"><a class="line" href="group__kernels.html#ga547ea94d882f809b7a33c63cdda4dd37">  163</a></span>RealMatrix <a class="code hl_function" href="group__kernels.html#ga3fafbf415f6fec4d166ade39dccbc01a" title="Calculates the kernel gram matrix between two data sets.">calculateMixedKernelMatrix</a>(</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a><span class="keyword">const</span>&amp; kernel,</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset1, </div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset2</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>){</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>    RealMatrix M;</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>    <a class="code hl_function" href="group__kernels.html#ga3fafbf415f6fec4d166ade39dccbc01a" title="Calculates the kernel gram matrix between two data sets.">calculateMixedKernelMatrix</a>(kernel,dataset1,dataset2,M);</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>    <span class="keywordflow">return</span> M;</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>}</div>
</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span> </div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span><span class="comment"></span> </div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span><span class="comment">/// \brief Efficiently calculates the weighted derivative of a Kernel Gram Matrix w.r.t the Kernel Parameters</span></div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span><span class="comment">///</span></div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment">/// The formula is \f$  \sum_i \sum_j w_{ij} k(x_i,x_j)\f$ where w_ij are the weights of the gradient and x_i x_j are</span></div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment">/// the datapoints defining the gram matrix and k is the kernel. For efficiency it is assumd that w_ij = w_ji.</span></div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="comment">///This method is only useful when the whole Kernel Gram Matrix neds to be computed to get the weights w_ij and</span></div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span><span class="comment">///only computing smaller blocks is not sufficient. </span></div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span><span class="comment">///  \param kernel the kernel for which to calculate the kernel gram matrix</span></div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span><span class="comment">///  \param dataset the set of points used in the gram matrix</span></div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span><span class="comment">///  \param weights the weights of the derivative, they must be symmetric!</span></div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span><span class="comment">///  \return the weighted derivative w.r.t the parameters.</span></div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span><span class="comment">///  \ingroup kernels</span></div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType,<span class="keyword">class</span> WeightMatrix&gt;</div>
<div class="foldopen" id="foldopen00186" data-start="{" data-end="}">
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"><a class="line" href="group__kernels.html#gafb6b639ff5daa090b08b13e97e78a7bc">  186</a></span>RealVector <a class="code hl_function" href="group__kernels.html#gafb6b639ff5daa090b08b13e97e78a7bc" title="Efficiently calculates the weighted derivative of a Kernel Gram Matrix w.r.t the Kernel Parameters.">calculateKernelMatrixParameterDerivative</a>(</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>        <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; kernel,</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>        <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset, </div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>        WeightMatrix <span class="keyword">const</span>&amp; weights</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>){</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>    std::size_t kp = kernel.<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>();</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>    RealMatrix block;<span class="comment">//stores the kernel results of the block which we need to compute to get the State :(</span></div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>    RealVector kernelGradient(kp);<span class="comment">//weighted gradient summed over the whole kernel matrix</span></div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>    kernelGradient.clear();</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>    </div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>    <span class="comment">//calculate the gradint blockwise taking symmetry into account.</span></div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>    RealVector blockGradient(kp);<span class="comment">//weighted gradient summed over the whole block</span></div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>    boost::shared_ptr&lt;State&gt; state = kernel.<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a9057a4a71b4d28febb171e09bbd22c07" title="Creates an internal state of the kernel.">createState</a>();</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>    std::size_t startX = 0;</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>    <span class="keywordflow">for</span> (std::size_t i=0; i&lt;dataset.<a class="code hl_function" href="group__shark__globals.html#gabd82edf467b9b82f4b0a1e70fd695311" title="Returns the number of batches of the set.">numberOfBatches</a>(); i++){</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        std::size_t sizeX= <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(i));</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>        std::size_t startY = 0;<span class="comment">//start of the current batch in y-direction</span></div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        <span class="keywordflow">for</span> (std::size_t j=0; j &lt;= i; j++){</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>            std::size_t sizeY= <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(j));</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>            kernel.<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(i), dataset.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(j),block,*state);</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>            kernel.<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a48557b9834bc06ccb4e005ce441904c8" title="Computes the gradient of the parameters as a weighted sum over the gradient of all elements of the ba...">weightedParameterDerivative</a>(</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>                dataset.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(i), dataset.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(j),<span class="comment">//points</span></div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>                subrange(weights,startX,startX+sizeX,startY,startY+sizeY),<span class="comment">//weights</span></div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>                *state,</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>                blockGradient</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>            );</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>            <span class="keywordflow">if</span>(i != j)</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>                kernelGradient+=2*blockGradient;<span class="comment">//Symmetry!</span></div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>            <span class="keywordflow">else</span></div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>                kernelGradient+=blockGradient;<span class="comment">//middle blocks are symmetric</span></div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>            startY+= sizeY;</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        }</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>        startX+=sizeX;</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>    }</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>    <span class="keywordflow">return</span> kernelGradient;</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>}</div>
</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span> </div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>}</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span><span class="preprocessor">#endif</span></div>
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